Breast-Cancer-Wisconsin-(Prognostic)-Data-Set

OpenML dataset with id 43757

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Full work available at URL: https://api.openml.org/data/v1/download/22102582/Breast-Cancer-Wisconsin-(Prognostic)-Data-Set.arff

Upload date: 24 March 2022

Dataset Characteristics

Number of features: 32 (numeric: 31, symbolic: 0 and in total binary: 0 )
Number of instances: 569
Number of instances with missing values: 569
Number of missing values: 569

Description

Context Data From: UCI Machine Learning Repository http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/wpbc.names Content "Each record represents follow-up data for one breast cancer

   case.  These are consecutive patients seen by Dr. Wolberg
   since 1984, and include only those cases exhibiting invasive
   breast cancer and no evidence of distant metastases at the
   time of diagnosis.

The first 30 features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. A few of the images can be found at http://www.cs.wisc.edu/street/images/

The separation described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes.

The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34].

The Recurrence Surface Approximation (RSA) method is a linear programming model which predicts Time To Recur using both recurrent and nonrecurrent cases. See references (i) and (ii) above for details of the RSA method.

This database is also available through the UW CS ftp server:

ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WPBC/

1) ID number 2) Outcome (R = recur, N = nonrecur) 3) Time (recurrence time if field 2 = R, disease-free time if

   field 2 = N)

4-33) Ten real-valued features are computed for each cell nucleus: a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension ("coastline approximation" - 1)"

Acknowledgements Creators: Dr. William H. Wolberg, General Surgery Dept., University of Wisconsin, Clinical Sciences Center, Madison, WI 53792 wolbergeagle.surgery.wisc.edu

W. Nick Street, Computer Sciences Dept., University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 streetcs.wisc.edu 608-262-6619

Olvi L. Mangasarian, Computer Sciences Dept., University of Wisconsin, 1210 West Dayton St., Madison, WI 53706 olvics.wisc.edu

Inspiration I'm really interested in trying out various machine learning algorithms on some real life science data.

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